Many breast cancer patients remain free of distant metastasis even without adjuvant chemotherapy. While standard clinical traits fail to identify these good prognosis patients with adequate precision, analyses of gene expression patterns in primary tumors have resulted in more successful diagnostic tests. These tests use continuous measurements of the mRNA concentrations of numerous genes to determine a risk of metastasis in lymph node negative breast cancer patients with other clinical traits. The decision to use adjuvant chemotherapy to treat early-stage breast cancer must balance the reduced risk of recurrence with chemotherapy's toxic effects. The National Surgical Adjuvant Breast and Bowel Project trials B-14 and B-20 suggest that 85% of node-negative, ER+ patients who are treated with tamoxifen alone will be disease free for 10 years (Fisher 2004). Treatment guidelines such as those from the St. Gallen consensus group (Goldhirsch 2005, Eifel 2001) identify a small percentage of patients who can safely forego chemotherapy; however under these guidelines, a significant number of patients undergo chemotherapy unnecessarily.
Methods of stratifying breast cancer patients according to relapse risk have been developed using multi-gene measures of mRNA concentrations. Two tests are the 21-gene screening panel, Oncotype DX® (Genomic Health, Redwood City, Calif.) (Paik 2004, Paik 2006), and the 70-gene array-based test Mammaprint® (Agendia, Amsterdam) (de Vijver 2002, Buyse 2006). These tests apply to node-negative tumors with various other clinical traits. The prospective clinical trial TAILORx (Zujewski 2008, Piccart-Gebhart 2007) is testing the ability of Oncotype DX® to identify patients who can safely forego chemotherapy. The MINDACT trial in Europe is a similar test of Mammaprint (Piccart-Gebhart 2007, Cardoso 2008). Both of these tests utilize continuous measurements of mRNA concentrations of numerous genes.
In the past few years, several groups have published studies concerning the classification of various cancer types by microarray gene expression analysis (see, e.g., Golub 1999, Bhattacharjae 2001, Chen-Hsiang 2001, Ramaswamy 2001). Certain classifications of human breast cancers based on gene expression patterns have also been reported (Martin 2000, West 2001, Sorlie 2001, and Yan 2001). However, these studies mostly focus on improving and refining the already established classification of various types of cancer, including breast cancer, and generally do not provide new insights into the relationships of the differentially expressed genes, and do not link the findings to treatment strategies in order to improve the clinical outcome of cancer therapy.
Although modern molecular biology and biochemistry have revealed hundreds of genes whose activities influence the behavior of tumor cells, state of their differentiation, and their sensitivity or resistance to certain therapeutic drugs, with a few exceptions, the status of these genes has not been exploited for the purpose of routinely making clinical decisions about drug treatments. One notable exception is the use of estrogen receptor (ER) protein expression in breast carcinomas to select patients for treatment with anti-estrogen drugs, such as tamoxifen. Another exceptional example is the use of ErbB2 (Her2) protein expression in breast carcinomas to select patients with the Her2 antagonist drug Herceptin™ (Genentech, Inc., South San Francisco, Calif.).
Despite recent advances, the challenge of cancer treatment remains to target specific treatment regimens to pathogenically distinct tumor types, and ultimately personalize tumor treatment in order to maximize outcome. Hence, a need exists for tests that simultaneously provide predictive information about patient responses to the variety of treatment options. This is particularly true for breast cancer, the biology of which is poorly understood. It is clear that the classification of breast cancer into a few subgroups, such as ErbB2+ subgroup, and subgroups characterized by low to absent gene expression of the estrogen receptor (ER) and a few additional transcriptional factors (Perou 2000) does not reflect the cellular and molecular heterogeneity of breast cancer, and does not allow the design of treatment strategies maximizing patient response.
In particular, once a patient is diagnosed with cancer, such as breast or ovarian cancer, there is a strong need for methods that allow the physician to predict the expected course of disease, including the likelihood of cancer recurrence, long-term survival of the patient, and the like, and select the most appropriate treatment option accordingly. To date, no set of satisfactory predictors for prognosis based on the clinical information alone has been identified.